code: def _get_inference_pipeline(model_path, spm_model_path):
      model = torch.jit.load(model_path)
      model.eval()
  
      sp_model = spm.SentencePieceProcessor(model_file=spm_model_path)
      token_processor = SentencePieceTokenProcessor(sp_model)
  
      decoder = torchaudio.models.RNNTBeamSearch(
          model.model, sp_model.get_piece_size()
      )
  
      return InferencePipeline(
          preprocessor=Preprocessing(),
          model=model,
          decoder=decoder,
          token_processor=token_processor,
      )
  
imports: import numpy
  import sentencepiece
  import torch
  import torchaudio
  import torchvision
  import sys
  from avsr.data_prep.detectors.mediapipe.detector import LandmarksDetector
  from avsr.data_prep.detectors.mediapipe.video_process import VideoProcess
  from torchaudio.utils import download_asset
label_desc: Use image node_id 2 for calling a global function with example usage: _get_inference_pipeline(model_path, spm_model_path) and returns: InferencePipeline